A watershed runoff reconstruction method based on stacked ensemble machine learning under incomplete data:A case study of Juzhanghe River
[Objective]To achieve runoff reconstruction under incomplete data,[Methods]a method is introduced to fill spatio-temporal gaps in water consumption data.Drawing on the water balance laws,a stacked machine learning model is created.The model is then applied to calculate the natural runoff of the Herong hydrological gauging station as a case study.Initially,a set of feature variables correlating with agricultural,industrial,and domestic water consumption is identified to create a comprehensive feature variable indicator system.The system uses intermittently available water consumption data as input into stacked ensemble machine learning model to produce a continuous water consumption dataset.Adhering to the water balance principle,natural run-off is calculated by adjusting measured runoff attributed to anthropogenic activities.[Results]The stacked ensemble machine learning model produced relative errors of 0.62%,0.03%,and 4.95%for agricultural,industrial,and domestic water,respec-tively.The average annual natural runoff at the Herong hydrological gauging station from 2002 to 2020 was 54.5 m3/s,with a natural runoff depth of 501.3 mm.[Conclusion]The proposed method enables the reconstruction of natural runoff in areas with missing water consumption data across temporal and spatial scales,and is of great significance for the comprehensive management and optimal allocation of regional water resources.
万方数据
维普
